The present invention relates to the field of visual recognition and classification of objects in video.
The field of computer object recognition is concerned with finding and identifying objects in an image or video sequence. Humans are capable of recognizing a multitude of objects in images quickly and accurately, and regardless of variations in, e.g., viewpoint, size, scale, and orientation. Objects can even be recognized by humans when they are partially obstructed from view. However, this task continues to be challenging for computer vision systems, and even more so when it comes to object recognition in moving visual media. Current object classification systems face difficulties in handling arbitrary camera angles and zooms, varying poses, occlusions, illumination conditions, and strong shadow effects, which may cause variations in object appearance, shape, and size.
Over the past several decades, many different approaches have been proposed to automatically classify objects in images and videos. However, these approaches often require large amounts of training data to learn robust classifiers, and typically suffer from object-pose variability. As a result, state-of-the-art visual classifiers include a high level of uncertainty.
The foregoing examples of the related art and limitations related therewith are intended to be illustrative and not exclusive. Other limitations of the related art will become apparent to those of skill in the art upon a reading of the specification and a study of the figures.